Abstract 5041: Multiple outcome linkage disequilibrium score regression for confounding independent genetic correlations
Authors: Rowland W. Pettit, Christopher I. Amos
Journal: Cancer Research (2022)

Abstract Introduction: Linkage disequilibrium score regression (LDSR) estimates the genetic correlation between traits. Currently genetic correlation is calculated in LDSR by regressing the product of genome wide association study SNP z-scores (Ztrait1 * Ztrait2) against the SNPs calculated linkage disequilibrium scores for a population. The slope of this regression accurately estimates the genetic covariance between the two traits. Genetic covariance is then normalized to a genetic correlation. No method exists for calculating multi-trait genetic correlations. Understanding the genetic correlation between two traits, conditional on the effects of a third trait, would help clarify the correlation in traits, after removing effects from confounding variables. Methods: To estimate genetic correlation between two traits independent of the effects of a third trait, we are developing a multiple outcome linkage disequilibrium score regression. To do this we set up a multivariate outcome regression on the linkage disequilbrium score, jointly modeling the products of z-scores for i) the trait of interest with the outcome of interest and ii) the trait of interest with the confouder. This process is similar to the steps of the usual implementation of LDSR, however in multiple outcome LDSR modeling the model coefficients, or the genetic covariances of the trait and the confounder will covary. This affords the opportunity for testing to jointly determine if the genetic covariances identified are jointly or independently significant. We have implemented this multiple outcome LDSR method and compared the results to standard outcome LDSR using data from the United Kingdom Biobank (UKBB) and the TRICL-OncoArray lung cancer (LC) consortium. We evaluated the UKBB trait of BMI and current smoking status, for their joint genetic correlation on overall LC outcomes. Results: Implementing LDSR, significant pairwise genetic correlation (rg) was observed between the UKBB trait of current smoking status, and the overall LC outcome (rg = 0.62 +/- 0.06, p = 7.17x10-25). Similarly, significant positive genetic correlation was observed between the UKBB trait of BMI and overall LC predisposition (rg = 0.20 +/- 0.03, p = 2.607x10-9). Using multiple outcome LDSR and jointly modeling BMI-LC susceptibility with smoking-LC susceptibility resulted in a BMI-LC genetic correlation of 0.0103, which was highly attenuated, and consistent with prior studies that show the BMI-LC association is modulated through smoking behavior. Conclusions: Multiple outcome linkage disequilibrium score regression allows for multiple-trait genetic covariances to be measured simultaneously and for effect estimates to be partially mitigated for the influence of a confounder. Further validation and sensitivity analysis is warranted to determine appropriate implementations and interpretations of this novel method. Citation Format: Rowland W. Pettit, Christopher I. Amos. Multiple outcome linkage disequilibrium score regression for confounding independent genetic correlations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5041.